Systems and methods for orchestration and optimization of wireless networks

ABSTRACT

A system described herein may provide for the use of artificial intelligence/machine learning (“AI/ML”) techniques to generate models for various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network. The system may determine Key Performance Indicators (“KPIs”) or other attributes that are of particular relevance or importance for a given sector model, and may determine actions to perform with respect to particular sectors in order to enhance performance according to the KPIs that are of particular relevance to a sector model determined with respect to the particular sectors.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation of U.S. patent application Ser. No.17/107,502, filed on Nov. 30, 2020, titled “SYSTEMS AND METHODS FORORCHESTRATION AND OPTIMIZATION OF WIRELESS NETWORKS,” the contents ofwhich are herein incorporated by reference in their entirety.

BACKGROUND

Wireless networks, such as Long-Term Evolution (“LTE”) networks, FifthGeneration (“5G”) networks, or the like, may include radio accessnetworks (“RANs”), via which user equipment (“UE”), such as mobiletelephones or other wireless communication devices, may receive wirelessservice. RANs, and/or portions of RANs, may have differentcharacteristics and/or may exhibit different performance metrics (e.g.,latency, throughput, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example overview of one or more embodimentsdescribed herein, in which a Global Optimization System (“GOS”) maydetermine a sector model, configuration framework, and/or set of actionsto perform with respect to a given sector associated with a RAN of awireless network;

FIG. 2 illustrates example configuration frameworks, sector models,and/or actions/parameters that may be generated, received, maintained,provided, etc. by a GOS of some embodiments;

FIGS. 3, 4A, and 4B illustrate examples of respective configurationframeworks in accordance with some embodiments;

FIG. 5 illustrates example attributes associated with a particularsector model, and further illustrates an example associations betweenthe sector model, configuration framework, and actions and/orparameters, in accordance with some embodiments;

FIGS. 6-8 illustrate an example determination of one or more sectormodels, configuration frameworks, and/or sets of actions to perform withrespect to a given sector associated with a RAN of a wireless network;

FIG. 9 illustrates an example process for determining one or more sectormodels, configuration frameworks, and/or sets of actions to perform withrespect to a given sector associated with a RAN of a wireless network,in accordance with some embodiments;

FIG. 10 illustrates an example environment in which one or moreembodiments, described herein, may be implemented;

FIG. 11 illustrates an example arrangement of a radio access network(“RAN”), in accordance with some embodiments;

FIG. 12 illustrates an example arrangement of an Open RAN (“0-RAN”)environment in which one or more embodiments, described herein, may beimplemented; and

FIG. 13 illustrates example components of one or more devices, inaccordance with one or more embodiments described herein.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

Embodiments described herein provide for the use of artificialintelligence/machine learning (“AUML”) techniques or other suitabletechniques to model attributes, characteristics, key performanceindicators (“KPIs”), and/or other information associated with variouslocations or regions associated with one or more RANs of a wirelessnetwork (e.g., a LTE network, a 5G network, and/or another type ofnetwork). As discussed herein, such locations or regions may be referredto as “sectors.” Further, in the examples discussed herein, sectors mayinclude evenly distributed areas of a uniform shape (e.g., a hexagon).In practice, sectors may be arranged or defined differently. Forexample, in some embodiments, sectors may be defined with respect to thelocation of one or more base stations of a RAN (e.g., where a sector maybe defined based on a coverage area of the one or more base stationsand/or may be defined based on a physical location at which one or moreantennas or other physical equipment of the base stations areinstalled), and/or may be defined independently of the location of theone or more base stations.

As described herein, one or more scores, metrics, etc. (referred toherein simply as “scores” for the sake of brevity) may be determined(e.g., using AI/ML techniques and/or other suitable techniques) based onservice coverage (e.g., range or area of wireless service), servicequality (e.g., Signal-to-Interference-and-Noise-Ratio (“SINR”), ChannelQuality Indicator (“CQI”), latency, throughput, etc.), energyconsumption metrics (e.g., measure of energy consumed over time),mobility metrics (e.g., quantity or proportion of UEs involved in ahandover process), and/or other suitable metrics or informationassociated with base stations or other equipment associated with theRANs. In some embodiments, the scores may reflect an overalloptimization score, which may reflect a holistic measure of how well agiven sector is optimized.

As described herein, different sectors may be associated with differentattributes, characterizations, categories, clusters, or the like.Embodiments herein may, for example, categorize a given sector as beingassociated with one or more sector models, where a sector model includesattributes, characteristics, etc. that may be compared to a given sectorto determine whether the sector model applies to the sector. Further,particular sector models may be associated with configurationframeworks, which may specify weights and/or other information that maybe used to generate an overall optimization score for a sector. Forexample, one particular configuration framework may weight energysavings metrics relatively heavily, while another configurationframework may weight energy savings metrics relatively lower than othertypes of metrics (e.g., coverage, quality, mobility, and/or othermetrics).

Further, as described herein, particular configuration frameworks may beassociated with particular sets of configuration parameters, attributes,and/or actions, which may be used by particular sectors in order tooptimize the operation of the sectors in accordance with optimizationgoals that are reflected by the weights in the configuration frameworks.As described herein, the association between particular sectorattributes, sector models, configuration frameworks, and/or associatedactions may be generated and/or refined using one or more AI/MLtechniques or other suitable techniques (e.g., deep learning, reinforcedor unreinforced machine learning, neural networks, K-means clustering,regression analysis, and/or other suitable techniques).

As shown in FIG. 1, for example, geographical area (or region) 100 maybe subdivided into a set of sectors 101. The set of sectors 101 mayinclude, as shown, sector 101-1, 101-2, and one or more additionalsectors that are not explicitly illustrated with a reference numeral.

Further in this example, each sector 101 may be associated withparticular base stations 103. For example, base station 103-1 may belocated in one particular sector 101, while base station 103-2 may belocated in another sector 101. Further, additional base stations 103(e.g., base stations not explicitly illustrated with a referencenumeral) may be present in geographical region 100. That is, thelocation of each base station 103 may be within a particulargeographical area (e.g., a hexagonal-shaped geographical area, in thisexample) that corresponds to a respective sector 101. For the sake ofexample, each sector 101 is associated with at least one base station103. In practice, one or more sectors 101 may not include any basestations 103.

As shown, Global Optimization System (“GOS”) 105 may receive (at 102)network KPIs and/or parameters associated with one or more sectors 101.For example, Global Optimization System 105 may communicate with basestations 103 of sectors 101 and/or UEs located within such sectors 101via an application programming interface (“API”), an X2 interface,and/or some other suitable communication pathway, in order to receivesuch information. For example, base stations 103 and/or UEscommunicatively coupled to respective base stations 103 may “push” suchinformation to Global Optimization System 105 (e.g., via the API) on aperiodic or intermittent basis, upon the occurrence of trigger events(e.g., one or more Quality of Service (“QoS”) metrics exceeding athreshold value, a connection or disconnection of one or more UEs to oneor more base stations 103, and/or other events), and/or on some otherbasis. In some embodiments, Global Optimization System 105 may “pull”(e.g., request or otherwise obtain) such information from the UEs, basestations 103, and/or other device or system that receives, collects,maintains, and/or provides such information. For example, GlobalOptimization System 105 may be communicatively coupled to a ServiceCapability Exposure Function (“SCEF”) of a core network associated withbase stations 103, a Network Exposure Function (“NEF”), and/or othersuitable device, system, function, etc.

The received KPIs and/or parameters may include, for example, KPIsrelated to coverage, quality, energy consumption, mobility, and/or othersuitable KPIs. Further, the received parameters may include, forexample, configuration parameters, inter-sector information, localefeatures, and/or other suitable information indicating parameters and/orcharacteristics of a given sector 101. More detailed examples of KPIsand/or parameters are described below.

As further shown, GOS 105 may determine (at 104) one or more sectormodels associated with respective sectors 101 based on the received KPIsand/or parameters. For example, as discussed below, GOS 105 may useAI/ML techniques or other suitable techniques to identify one or moresector models that includes KPIs and/or attributes that are similar tothe KPIs and/or attributes (received at 102) associated with respectivesectors 101. For example, when determining whether KPIs and/orattributes of a given sector model is “similar” to KPIs and/orattributes of a given sector 101, GOS 105 may generate one or morescores, classifiers, or the like, and/or may perform a suitablesimilarity analysis to determine a measure of similarity between KPIsand/or attributes of a set of sector models and KPIs and/or attributesof a given sector 101. In some embodiments, GOS 105 may select aparticular sector model if the measure of similarity exceeds a thresholdmeasure of similarity. Additionally, or alternatively, GOS 105 mayselect a particular quantity of highest-scoring sector models (e.g., thehighest scoring sector mode, the top three scoring sector models, etc.).In some embodiments, GOS 105 may select a particular quantity ofhighest-scoring sector models, so long as the scores associated withsuch sector models exceeds a threshold score (e.g., the top threescoring sector models so long as the top three scoring sector modelsexceed the threshold score, the top two scoring sector models if thethird highest-scoring sector model is below the threshold score, etc.).

As further discussed in more detail below, GOS 105 may further determine(at 104) one or more configuration frameworks for one or more sectors101 based on the sector models identified with respect to respectivesectors 101. For example, as discussed below, particular sector modelsmay be associated with particular configuration frameworks. In someembodiments, a given sector model may be associated with an affinityscore for multiple configuration frameworks, where the affinity scoreindicates a measure of affinity, correlation, effectiveness,applicability, or the like of a given configuration framework to a givensector model. For example, the same configuration framework may beparticularly applicable to one particular sector model (e.g., associatedwith a relatively high affinity score with respect to the particularsector model), while the same configuration framework may be lessapplicable to a different sector model (e.g., associated with arelatively low affinity score with respect to the other sector model).In some embodiments, GOS 105 may generate a configuration frameworkbased on using AI/ML techniques or other suitable techniques to analyzethe sector models, KPIs, and/or parameters associated with sector 101,as well as analyzing previously generated configuration frameworks, todetermine weights and/or other parameters of a configuration frameworkthat is applicable to the particular sector 101.

In some embodiments, GOS 105 may receive (at 102) KPIs and/or parametersover time, and may select (at 104) different sector models and/orconfiguration frameworks based on different KPIs and/or parametersreceived at different times and/or time periods. As one example, aparticular sector 101 may exhibit a first set of KPIs and/or parameters(e.g., latency, throughput, quantity of connected UEs, and/or other KPIsor parameter) at times corresponding to a morning or afternoon weekdaycommute, and may exhibit a second set of KPIs and/or parameters at timescorresponding to an evening or weekend. In this example, GOS 105 maydetermine (at 104) a first sector model (or set of sector models) andone or more associated configuration frameworks during morning orafternoon hours on weekdays, and may determine a second sector model (orset of sector models) and one or more associated configurationframeworks during evening hours and/or weekends.

GOS 105 may further output (at 106) information indicating theidentified configuration frameworks to respective sectors 101. Forexample, GOS 105 may provide the information to respective base stations103 associated with sectors 101, to a management device or systemassociated with one or more sectors 101, and/or some other device orsystem. Additionally, or alternatively, GOS 105 may provide (at 106)information indicating one or more actions associated with theidentified configuration frameworks. For example, in some embodiments,GOS 105 may determine, based on the sector model(s), KPIs, and/orparameters associated with a respective sector 101, and further based onthe identified configuration framework(s) selected for sector 101, oneor more actions to take to increase the overall optimization scoreassociated with sector 101. Additionally, or alternatively, each sector101 may determine particular actions to take based on the receivedconfiguration framework information. As noted above, such actions may beselected and performed by network devices located in or serving sector101 in order to increase the overall optimization score associated withsector 101. For the sake of brevity, the performance of a given actionby a network device located in or serving sector 101 will be referred toherein as sector 101 performing the action.

Such actions may include, for example, modifying QoS-related parameters,such as modifying queue weights associated with the processing,transmitting, or otherwise handling traffic associated with particularQoS values, such as QoS Class Identifier (“QCI”) values, QoS FlowIdentifier (“QFI”) values, priority values, and/or other suitable valuesor indicators.

In some embodiments, such actions may include modifying the availabilityor allocation of physical RAN resources, such as Physical ResourceBlocks (“PRBs”), portions of radio frequency (“RF”) spectrum, or thelike. In some embodiments, such modification may be on the basis of anidentifier associated with a given UE, such as an International MobileSubscriber Identity (“MR”), International Mobile Station EquipmentIdentity (“IMEI”), Globally Unique Temporary Identifier (“GUTI”),Subscription Permanent Identifier (“SUPI”), Internet Protocol (“IP”)address, Mobile Directory Number (“MDN”), or other suitable identifier.For example, a particular UE or set of UEs, such as UEs associated withfirst responders, government agencies, or some other suitable category,may be granted a larger allocation of available RF resources than otherUEs.

In some embodiments, such actions may include implementing one or moreenergy-saving techniques, such as activating a cell suspend mode,modifying antenna transmission and/or reception parameters in the timeand/or frequency domains, throttling one or more processors, entering alow-power mode, and/or otherwise reducing the amount of power (e.g.,electrical power) consumed by one or more devices or systems thatimplement or are otherwise associated with sector 101.

In some embodiments, such actions may include modifying one or morebeamforming parameters associated with sector 101. For example, sector101 may modify azimuth angle, tilt angle, beam width, antenna power,and/or other aspects of beamforming parameters associated with one ormore antennas of sector 101. In some embodiments, sector 101 may modifya Multiple-Input Multiple-Output (“MIMO”) configuration associated withsector 101, such as activating or deactivating a MIMO mode, selectingone or more antennas to implement MIMO a given MIMO configuration, orother MIMO parameters associated with sector 101.

In some embodiments, such actions may include modifying parametersrelated to handovers and/or mobility. For example, sector 101 may modifya Neighbor Cell List (“NCL”) provided to UEs connected to a particularbase station of sector 101, which may affect how such UEs scan for ordetect neighboring base stations. In some embodiments, such actions mayinclude modifying handover-related parameters, such as handoverthresholds used by UEs to determine whether such UEs should request ahandover from base station to another base station. Such handoverthresholds may refer to, for example, threshold measures of signalstrength or quality, such as a Received Signal Strength Indicator(“RSSI”) value, a CQI value, a Signal-to-Interference-and-Noise-Ratio(“SINR”) value, a Reference Signal Receive Power (“RSRP”) value, and/orsome other suitable value.

While example actions are described above, in practice, other suitableactions may be performed in order to optimize the operation of sectors101 (e.g., based on configuration frameworks identified with respect tosectors 101). In some embodiments, performing such actions may includeperforming multiple actions, such as multiple actions performed above.Such performance may be simultaneous, sequential, or on some otherbasis.

Respective sectors 101 may perform (at 108) the actions associated withrespective configuration frameworks, and GOS 105 may continue to receive(at 102) up-to-date KPIs associated with sectors 101. GOS 105 may, basedon continuing to receive the up-to-date KPIs, modify the determinationof sector models associated with a particular sector 101, and/or mayupdate an overall optimization score associated with the particularsector 101. In some embodiments, GOS 105 may select a new set of actionsand/or a different configuration framework for sector 101 based on theup-to-date KPIs. In some embodiments, GOS 105 may modify one or moresector models, configuration frameworks, and/or other information basedon whether the performed (at 108) actions increased the overalloptimization score associated with sector 101, and/or based on how muchthe overall optimization score associated with sector 101 was modifiedbased on the performance of the actions.

In some embodiments, while described in the context of being performedby GOS 105, in some embodiments, one or more devices or systemsassociated with sectors 101 may perform one or more of the operationsdescribed above in lieu of, or in addition to, GOS 105. For example, insome embodiments, one or more devices or systems of sector 101 mayidentify a particular action based on a given configuration frameworkand/or sector model, and/or based on continuing to monitor KPIsassociated with sector 101 after performing (at 108) a particular actionor set of actions.

FIG. 2 illustrates example configuration frameworks, sector models,and/or actions/parameters that may be generated, received, maintained,provided, etc. by GOS 105. For example, GOS 105 may be associated with aset of configuration frameworks 201, such as example configurationframeworks 201-1, 201-2, and 201-L. Further, GOS 105 may be associatedwith a set of sector models 203, such as example sector models 203-1,203-2, and 203-M. Additionally, GOS 105 may be associated with a set ofactions/parameters 205, such as example actions/parameters 205-1, 205-2,and 205-N.

GOS 105 may generate and/or modify configuration frameworks 201, sectormodels 203, and/or actions/parameters 205 based on AI/ML techniques orother suitable techniques. For example, GOS 105 may generate, modify,refine, etc. configuration frameworks 201, sector models 203, and/oractions/parameters 205 based on an evaluation of real-world data fromsectors 101 and/or simulations of KPIs in a simulation and/or testenvironment. GOS 105 may further determine or identify correlationsbetween respective configuration frameworks 201, sector models 203,and/or actions/parameters 205 using AI/ML techniques or other suitabletechniques.

FIGS. 3, 4A, and 4B, discussed below, illustrate examples of respectiveconfiguration frameworks 201. FIG. 5, also discussed below, illustratesexample attributes associated with a particular sector model 203, andfurther illustrates an example association between sector model 203,configuration framework 201, and/or actions/parameters 205.

As shown in FIG. 3, for example, configuration framework 301 may includea set of KPIs, parameters, characteristics, etc. 303 (e.g., KPIs,parameters, characteristics, etc. 303-1 through 303-Q). KPIs,parameters, characteristics, etc. 303 may include any suitable type ofKPIs, parameters, characteristics, etc. 303 relating to actions to beperformed in a network environment, in order to optimize considerationsrelating to KPIs, parameters, characteristics, etc. 303 in a mannersimilar to that described above. For the sake of brevity, sets of KPIs,parameters, characteristics, etc. 303 are sometimes referred to hereinas “KPI categories 303.”

In some embodiments, each KPI category 303 of configuration framework301 may specify one or more KPIs, such as latency, throughput, jitter,quantity of active connections, quantity and/or proportion of droppedcalls, energy consumption metrics, quantity or proportion of handoversinto and/or out of a sector, durations of connections of UEs to basestations located in a sector, location information of UEs connected to abase station of a sector, and/or other suitable metrics. In someembodiments, each respective KPI category 303 may specify conditions,thresholds, or the like, based on which a given KPI category 303 may beapplicable to a particular KPI or set of KPIs associated with a givensector. For example, KPI category 303-1 and KPI category 303-2 may bothbe associated with different value ranges for the same KPI. For example,KPI category 303-1 may be applicable to latency metrics if such latencymetrics indicate a value of 100 milliseconds (“ms”) or lower, and KPIcategory 303-2 may be applicable to latency values of greater than 100ms.

In some embodiments, KPI categories 303 may include thresholds based onwhich maximum and/or minimum scores may be determined. For example,assume that KPI category 303-1 is applicable to a latency metric. KPIcategory 303-1 may specify that latency values below a first thresholdvalue, such as 30 ms, are associated with a maximum KPI score (e.g., 100out of 100), may specify that latency values above a second thresholdvalue (e.g., 300 ms) are associated with a minimum KPI score (e.g., 1out of 100), etc. In this manner, certain KPIs may be prioritized by KPIcategory 303, without allowing such KPIs to dominate the overalloptimization score for a given sector.

As further shown, each respective KPI category 303 may be associatedwith a score weight. For example, KPI category 303-1 may be associatedwith a first weight W1, KPI category 303-2 may be associated with asecond weight W2, KPI category 303-3 may be associated with a thirdweight W3, and so on. As noted above, the different weights may be usedto prioritize certain KPIs or sets of KPIs more heavily than others.

For example, assume that configuration framework 301 has been selectedwith respect to a given sector 101. Sector 101 may generate, determine,etc. a set of KPIs 305 over a given time period. Sector 101, GOS 105,and/or some other suitable device or system may perform an overalloptimization score generation operation 307 with respect to sector 101for the given time period, based on the set of KPIs 305 and the weightsassociated with respective KPIs. For example, assume that the set ofKPIs 305 include latency metrics associated with sector 101 and energyconsumption metrics associated with sector 101. Further assume thatlatency is a particular KPI identified in KPI category 303-1, and thatenergy consumption is a particular KPI identified in KPI category 303-2.

When generating (at 307) an overall optimization score for sector 101, afirst KPI score may be generated based on the received latency metricsand a second KPI score may be generated based on the energy consumptionmetrics. In some embodiments, KPI score generation may be in anormalized manner (e.g., on a scale of 1-100 or some other suitablescale) for dissimilar or incongruous metrics. For example, a latencyvalue of 10 ms may be associated with a KPI score of 92, and an energyconsumption value of 10 kiloWatt-hours (“kWh”) may be associated with aKPI score of 15. In some embodiments, particular KPI categories 303 mayspecify formulas, rubrics, rules, or the like based on which respectiveKPI scores may be generated based on raw KPI values. These KPI scoresmay be further modified based on the respective weights associated withKPI categories 303. For example, the first KPI score may be weightedaccording to weight W1, while the second KPI score may be weightedaccording to weight W2. In some embodiments, weighting the KPI scoresmay include multiplying the respective KPI scores by the weights inorder to generated weighted KPI scores. In this manner, different KPIsor sets of KPIs may be factored or prioritized differently in theoverall optimization score ultimately generated via overall optimizationscore operation 307.

Overall optimization score operation 307 may include aggregating,combining, and/or performing other suitable computations on weighted KPIscores to generate overall optimization score 309 for sector 101. Asnoted above, overall optimization score 309 may be generated and/ormodified on an ongoing basis, as updated sector KPIs 305 are received ordetermined.

FIGS. 4A and 4B illustrate example configuration frameworks 401 and 405,in accordance with some embodiments. As shown in FIG. 4A, configurationframework 401 may include KPI categories such as KPI categories 403-1through 403-3.

KPI category 403-1 may include, for example, KPIs related to coverageand/or quality, such as latency, throughput, jitter, SINR, CQI, RSSI,etc. In some embodiments, such KPIs may be specified as a function oflocation within a sector and/or distance from one or more base stationsof a sector. For example, KPI category 403-1 may specify a first set ofthresholds, values, formulas, etc. for calculating a KPI score relatingto latency within 100 meters of a base station, and a second set ofthresholds, values, formulas, etc. for calculating a KPI score relatingto latency further than 100 meters from a base station.

KPI category 403-1 may, in some embodiments, include KPIs, metrics,parameters, etc. relating to RAN coverage within a given geographicalarea and/or sector 101. For example, KPI category 403-1 may relate toareas within sector 101 that receive wireless coverage from basestations or other wireless network infrastructure located within orotherwise serving sector 101. In some embodiments, KPI category 403-1may include quality metrics as a function of distance or coverage (e.g.,distance from a base station and/or other RF hardware), in that KPIs,metrics, etc. relating to signal quality may vary as a function oflocation (e.g., different levels or qualities of coverage may beavailable at different locations within sector 101). Such KPIs, metrics,etc. may include a RSRP value (e.g., a mean RSRP value over a given timeperiod, a maximum RSRP value over a given time period, a minimum RSRPvalue over a given time period, etc.), a Reference Signal ReceivedQuality (“RSRQ”) (e.g., a mean RSRQ value over a given time period, amaximum RSRQ value over a given time period, a minimum RSRQ value over agiven time period, etc.), a Channel Quality Indicator CQI value (e.g., amean CQI value over a given time period, a maximum CQI value over agiven time period, a minimum CQI value over a given time period, etc.),an uplink (“UL”) power headroom value (e.g., a mean UL power headroomvalue over a given time period, a maximum UL power headroom value over agiven time period, a minimum UL power headroom value over a given timeperiod, etc.), an UL Physical Uplink Shared Channel (“PUSCH”)Signal-to-Interference-and-Noise-Ratio (“SINR”) value (e.g., a mean ULPUSCH value over a given time period, a maximum UL PUSCH value over agiven time period, a minimum UL PUSCH value over a given time period,etc.), a UL Physical Uplink Control Channel (“PUCCH”) value (e.g., amean UL PUCCH value over a given time period, a maximum UL PUCCH valueover a given time period, a minimum UL PUCCH value over a given timeperiod, etc.), quantity or percentage of samples with a giventransmission (“Tx”) mode, MIMO utilization values, and/or other suitableKPIs, values, metrics, or the like. As noted above, such KPIs, values,metrics, or the like may be monitored, provided, etc. in alocation-based manner. For example, performance KPIs associated withparticular sectors 101 and/or sub-sectors (e.g., divided according to“bins” or categories based on distance and/or angle within a givensector 101) may be evaluated in accordance with one or more weightsassociated with KPI category 403-1.

KPI category 403-2 may relate to metrics relating to mobility, such asquantity or proportion of handovers of UEs into or out of a sector,durations of connections between UEs and base stations located in asector, durations that UE location information indicated that UEs werelocated within a sector, or the like. In some embodiments, suchinformation may include, for example, whether a given UE is movingwithin sector 101, moving between sectors, and/or is stationary. KPIcategory 403-2 may further include and/or may be based on trends,weights, constraints, predictions, etc. relating to mobility, such aswhether a given UE is likely to enter, exit, traverse within, and/orremain stationary within sector 101. For example, such trends, weights,predictions, etc. may be generated, derived, calculated, computed, etc.based on one or more AI/ML techniques or other suitable techniques. Suchtechniques may include deep learning, reinforced or unreinforced machinelearning, neural networks, K-means clustering, regression analysis,and/or other suitable techniques, analyses, computations, or the like.In some embodiments, KPI category 403-2 may include KPIs, metrics,values, etc. relating to a quantity of inter-RAT handovers occurring ina particular geographical area (e.g., sector 101) or with respect to arespective base station over a given time period, quantity of inter-celltype sessions over a given time period, quantity of co-sectortransitions over a given time period, quantity of intra-frequencyhandovers over a given time period, quantity of inter-frequencyhandovers over a given time period, quantity of blind redirections overa given time period, and/or other suitable KPIs, metrics, and/or otherinformation.

KPI category 403-3 may relate to metrics related to amounts or rates ofenergy consumption (e.g., electrical power usage) at one or more devicesor systems of a given sector, such as rates of consumption (e.g., Watts,kiloWatts, etc.), amounts of consumption over time (e.g., Wh, kWh,etc.), and/or other metrics related to energy consumption.

Further, in the example of FIG. 4A, KPI category 403-1 may be associatedwith a first weight W1, KPI category 403-2 may be associated with asecond weight W2, and KPI category 403-3 may be associated with a thirdweight W3. In the example configuration framework 405 of FIG. 4B, thesame KPI categories 403-1, 403-2, and 403-3 may be specified, but withdifferent weights than the example configuration framework 401 of FIG.4A. For example, configuration framework 405 may specify that KPIcategory 403-2 is associated with a fourth weight W4, KPI category 403-3is associated with a fifth weight W5, and KPI category 403-1 isassociated with a sixth weight W6.

FIG. 5 illustrates example sector attributes, metrics, or the like thatmay be associated with particular sector models 203, as well as anexample association between sector model 203, configuration framework201, and/or actions/parameters 205. As shown, for example, examplesector model 203 may include QoS metrics 501, energy consumption metrics503, RAN configuration parameters 505, inter-sector information 507,locale features 509, and/or one or more other types of information.

As discussed above, QoS metrics 501 may reflect QoS metrics associatedwith a particular sector 101 over a particular period of time, andenergy consumption metrics 503 may indicate an amount of energy consumedat the particular sector 101 over the particular period of time. RANconfiguration parameters 505 may include parameters such as anindication of quantity and/or position (e.g., geographical position) ofphysical infrastructure hardware (e.g., antennas, radios, data centers,or the like) associated with one or more RANs in sector 101. In someembodiments, RAN configuration parameters 505 may indicate particularradio access technologies (“RATs”) implemented in sector 101 (e.g., aLTE RAT, a 5G RAT, etc.), beam configurations implemented in sector 101(e.g., beam quantity, beam azimuth angles, beam width, beam transmissionpower, etc.), MIMO configuration information, and/or other suitableinformation.

Inter-sector information 507 may include information associated withsectors adjacent to or proximate to a given sector 101. For example,inter-sector information 507 may include RAN parameters, QoS metrics,and/or energy consumption metrics, associated with sectors adjacent toor within a threshold distance of sector 101. In some embodiments,inter-sector information 507 may include mobility information, which maybe associated with mobility of UEs between sector 101 and neighboringsectors. For example, inter-sector information 507 may indicate that UEsthat are located in sector 101 are likely to be stationary within sector101 for a first duration of time (e.g., approximately one hour), andthen that such UEs travel to a particular neighboring sector. As anotherexample, inter-sector information 507 may indicate that UEs that arelocated in the neighboring sector are relatively likely to enter theparticular sector 101.

Locale features 509 may include information indicating attributes and/orfeatures of the geographical area. For example, locale features 509 mayinclude information relating to building layout and/or density,topographical features (e.g., mountains, valleys, forests, streams,etc.), weather-related information, air quality-related information(e.g., smog density, particulate density, fog density, etc.), and/orother factors that may affect energy consumption, QoS metrics, or othermetrics. Locale features 509 may include geographical coordinates (e.g.,latitude and longitude coordinates, Global Positioning System (“GPS”)coordinates, or the like) or other suitable location information, toindicate the geographical locations of respective features.

As described below with respect to FIG. 6, a given sector 101 may beassociated with one or more sector models 203 based on a comparison ofthe above-described factors, and/or one or more other factors, of sector101 to such factors associated with a set of candidate sector models203. Briefly, for example, GOS 105 may determine that a particularsector 101, that exhibits a particular set of QoS metrics 501, aparticular set of energy consumption metrics 503, and a first set oflocale features 509 (e.g., urban features such as high-rise buildings)is associated with a first sector model 203, while another sector 101,that exhibits a similar set of QoS metrics 501 and a similar set ofenergy consumption metrics 503, but a different second set of localefeatures 509 (e.g., rural features such as relatively flat areas withrelatively low building density) is associated with a different secondsector model 203. Generally, a given sector model 203 may describe orreflect parameters, metrics, attributes, etc. of a given sector 101.

As further shown, sector model 203 may be associated with one or moreconfiguration frameworks 201. For example, GOS 105 may use AI/MLtechniques or other suitable techniques to determine that particularKPIs, attributes, etc. are more important for sectors 101 withparticular attributes than for sectors with different attributes. As anexample, GOS 105 may determine that sectors 101 having a first set ofattributes should have mobility-related parameters prioritized (e.g.,KPI category 403-2) and that energy consumption-related parameters(e.g., KPI category 403-3) are less of a priority, and may determinethat sectors 101 having a second set of attributes should havecoverage/quality-related parameters prioritized (e.g., KPI category403-1). In this example, GOS 105 may determine that the first sector 101is associated with a first configuration framework 201 and that thesecond sector 101 is associated with a second configuration framework201.

In some embodiments, additionally, or alternatively, GOS 105 maydetermine that the first and second sectors 101 are both associated withthe first and second configuration frameworks 201, but with differentsector-framework affinity scores 511. For example, the first sector 101may have a relatively high sector-framework affinity score 511 with thefirst configuration framework 201 (e.g., based on the determination thatconfiguration framework 201 prioritizes KPIs, metrics, or the like thatare a priority for the first sector 101) and may have a relatively lowsector-framework affinity score 511 with the second configurationframework 201. On the other hand, the second sector 101 may have arelatively low sector-framework affinity score 511 with the firstconfiguration framework 201 and a relatively high sector-frameworkaffinity score 511 with the second configuration framework 201.

GOS 105 may further generate, maintain, refine, etc. (e.g., using one ormore AI/ML techniques or other suitable techniques) one or moreassociations between respective configuration frameworks 201 and one ormore sets of actions/parameters 205. For example, each configurationframework 201 may be associated with one or more sets ofactions/parameters 205, as each particular set of actions/parameters 205may have been determined (e.g., based on real-world results and/orsimulated results) as increasing an overall optimization score of one ormore sectors 101 that match sector model 203, where such overalloptimization score is computed based on configuration framework 201. Asnoted above, actions/parameters 205 may include modifying QoSparameters, modifying beamforming and/or other antenna parameters,modifying energy consumption parameters, modifying handover parameters,or other suitable actions.

GOS 105 may also determine framework-action affinity scores 513 betweenconfiguration framework 201 and respective sets of actions/parameters205. As similarly discussed above, framework-action affinity scores 513may generally indicate how effective a given set of actions/parameters205 are for increasing an overall optimization score of a particularsector 101, given configuration framework 201 associated with sector101. Thus, multiple sets of actions/parameters 205 may be associatedwith a respective configuration framework 201, and respectiveframework-action affinity scores 513 between configuration framework 201and the sets of actions/parameters 205 may be used to ultimatelydetermine which actions to take with respect to a given sector model203.

While FIG. 5 provides examples of relationships between configurationframeworks 201, sector models 203, and actions/parameters 205, inpractice, other arrangements or relationships are possible. For example,in some embodiments, one or more affinity scores may be generated,maintained, refined, etc. between sector models 203 and sets ofactions/parameters 205. Such affinity scores between sector models 203and actions/parameters 205 may be determined in addition to, or in lieuof, sector-framework affinity scores 511 and/or framework-actionaffinity scores 513. For example, the same configuration framework 201may be applied to two different sector models 203 for two differentsectors 101, and the resulting actions/parameters 205 may be differentfor the two different sectors 101.

FIGS. 6-8 illustrate an example determination of one or moreconfiguration frameworks 201 and sector models 203 for a particularsector 101, and the performance of one or more actions 205 based on thedetermined configuration frameworks 201 and/or sector models 203. Asshown in FIG. 6, for example, GOS 105 may determine (at 602) parametersand/or attributes of sector 101. As discussed above, such parametersand/or attributes may include QoS metrics 501, energy consumptionmetrics 503, RAN configuration parameters 505, inter-sector information507, locale features 509, and/or other suitable parameters, attributes,metrics, or the like. GOS 105 may further identify (at 604) one or moresector models 203 based on the determined parameters and/or attributesof sector 101.

In this example, GOS 105 may determine that sector 101 is associatedwith a “highway” sector model 601-1 and a “media streaming” sector model601-3. As further shown, GOS 105 may not determine that sector 101 isassociated with an example “office building” sector model 601-2, or anexample “dense buildings” sector model 601-4. For example, GOS 105 maydetermine, based on a suitable similarity analysis of the parametersand/or attributes of sector 101, that sector models 601-2 and 601-4 donot match (e.g., correspond with a measure of similarity above athreshold measure of similarity) sector models 601-2 and 601-4, and/orthat sector models 601-1 and 601-3 match (e.g., have a higher measure ofsimilarity with) the parameters and/or attributes of sector 101 moreclosely. As discussed above, operations 602 and 604 may be performed onan ongoing basis, such that the selection of particular sector models601 may change based on updated parameters and/or attributes received byGOS 105 over time.

As shown in FIG. 7A, GOS 105 may identify (at 706) one or moreconfiguration frameworks 201 that are associated with identified sectormodels 601-1 and 601-3. For example, as discussed above, sector models601-1 and 601-3 may each be associated with one or more configurationframeworks 201. As also discussed above, one or more sector-frameworkaffinity scores 511 between respective configuration frameworks 201 andsector models 601-1 and 601-3 may be used to determine the set ofconfiguration frameworks 201 that are associated with sector models601-1 and 601-3 (e.g., configuration frameworks 201 with the highestsector-framework affinity scores 511, configuration frameworks 201 withsector-framework affinity scores 511 above a threshold score, etc.). Inthis example, sector model 601-1 is associated with configurationframeworks 201-1, 201-2, and 201-3, while sector model 601-3 isassociated with configuration framework 201-3 and configurationframework 201-4.

GOS 105 may select (at 708) a particular configuration framework 201based on the identified sets of configuration frameworks 201. In thisexample, GOS 105 may select (at 708) configuration framework 201-3 basedon configuration framework 201-3 being the only respective configurationframework 201 that has been identified with respect to both sectormodels 601-1 and 601-3. In some embodiments, GOS 105 may selectconfiguration framework 201-3 based on a cumulative, aggregate, etc.sector-framework affinity score 511 associated with each configurationframework 201 with respect to each sector model 601. In this example,configuration framework 201-3 have the highest cumulative, aggregate,etc. sector-framework affinity score 511 based on aggregatingsector-framework affinity score 511 between configuration framework201-3 and sector model 601-1, and between configuration framework 201-3and sector model 601-3. While particular examples of the selection ofconfiguration framework 201-3 are described above, in practice, GOS 105may use any suitable selection process or criteria to selectconfiguration framework 201-3 in the example of FIG. 7A.

FIG. 7B illustrates an example generation and/or selection of aparticular configuration framework 201 to apply to sector 101. Forexample, as similarly discussed above with respect to FIG. 7A, GOS 105may identify (at 706) one or more configuration frameworks 201 that areassociated with identified sector models 601-1 and 601-3. In thisexample, in lieu of selecting one of the identified configurationframeworks 201 (e.g., one of configuration frameworks 201-1 through201-4), GOS 105 may select or generate a different configurationframework 201-5. For example, GOS 105 may determine that a cumulative,aggregate, average, etc. of one or more of the sector-framework affinityscores 511 associated with the identified (at 706) configurationframeworks 201 does not meet a threshold score. Additionally, oralternatively, GOS 105 may compare sector models 601-1 and 601-3 anddetermine that a measure of similarity of these sector models 601 doesnot meet a threshold measure of similarity, and/or that a measure ofdissimilarity of these sector models 601 meets a threshold measure ofdissimilarity.

In some embodiments, GOS 105 may use one or more AI/ML techniques toselect and/or generate configuration framework 201-5. For example, GOS105 may generate or select configuration framework 201-5 based onidentifying features, attributes, etc. of one or more of configurationframeworks 201-1 through 201-4, and identifying common features,attributes, etc. based on which configuration framework 201-5 may begenerated or selected. In some embodiments, GOS 105 may additionally, oralternatively, generate and/or select configuration framework 201-5based on features, attributes, or the like of sector models 601-1 andsector model 601-3.

As shown in FIG. 8, GOS 105 may provide (at 812) the selectedconfiguration framework 201 and/or one or more actions/parameters 205associated with configuration framework 201, to sector 101. For example,as discussed above, configuration framework 201 may be associated withone or more sets of actions/parameters 205, which may be identified byGOS 105. In some embodiments, sector 101 may instead identify a set ofactions/parameters 205 based on the provided configuration framework201. Sector 101 may further implement (at 814) the received actionsand/or parameters. For example, sector 101 may modify antenna parameters(e.g., tilt angle, azimuth angle, etc.), QoS parameters (e.g., queueweights, resource allocation parameters, etc.), and/or other suitableactions and/or parameters as discussed above. Further, sector 101 maycontinue to “fine tune” the actions and/or parameters based on acontinued monitoring of KPIs or other metrics associated with sector101, such that the actions/parameters 205 associated with sector 101 maybe more precisely customized for the exact attributes, KPIs, etc. ofsector 101 than configuration framework 201. Additionally, oralternatively, GOS 105 may “fine tune” the actions and/or parameters,and/or modify associations (e.g., affinity scores) between sector 101and one or more configuration frameworks 201, sector models 203, and/oractions/parameters 205. GOS 105 may also refine associations betweenconfiguration frameworks 201, sector models 203, and/oractions/parameters 205 based on the continued monitoring.

While the examples above are provided in the context of configurationframework 201, sector model 203, and actions/parameters 205 beingdetermined for a particular sector 101, in practice, the same or similaroperations may be performed (e.g., concurrently, synchronously,asynchronously, sequentially, etc.) with respect to multiple sectors101. Further, in some embodiments, a particular sector model 203 (andassociated configuration frameworks 201 and/or actions/parameters 205)may be determined for an aggregate of multiple sectors (e.g., congruoussectors, adjacent sectors, sectors within a threshold proximity of eachother, sectors within a given geographical region, etc.). Such aggregatemay be referred to as a “super sector,” and/or the constituent sectors101 may be referred to as “sub-sectors.” In such embodiments, aparticular sector model 203 for a given super sector may be generatedbased on an aggregate, average, median, maximum, minimum, or othercomputed value associated with the KPIs, parameters, etc. of thesub-sectors. In some embodiments, sector model 203 for a given supersector may be based on the sector models 203 associated with respectivesub-sectors. For example, sector model 203 for the super sector may bebased on selecting one or more sector models 203 of the sub-sectors,and/or selecting or generating a different sector model 203 (e.g., in amanner similar to that described above with respect to FIG. 7B).

In some embodiments, the operations described above may be performed inan iterative and/or prioritized manner. For example, GOS 105 may rank aset of sectors 101 based on overall optimization scores, and may provideactions/parameters to sectors 101 in an order based on the ranking. Forexample, GOS 105 may select a lowest-scoring sector 101 and may performoperations described above in order to attempt to improve the overalloptimization score associated with the lowest-scoring sector 101 (andmay continue in this manner in order to address the particular sectors101 most in need of remedial action).

FIG. 9 illustrates an example process 900 for determining one or moresector models 203, configuration frameworks 201, and/or sets of actions205 to perform with respect to a given sector 101. In some embodiments,some or all of process 900 may be performed by GOS 105. In someembodiments, one or more other devices may perform some or all ofprocess 900 in concert with, and/or in lieu of, GOS 105, such as one ormore devices or systems associated with one or more sectors 101.

As shown, process 900 may include generating, receiving, and/ormodifying (at 902) one or more sector models 203 based on metrics,parameters, etc. associated with one or more sectors 101 of a wirelessnetwork. For example, as discussed above, GOS 105 may use AI/MLtechniques or other suitable techniques to generate and/or refine sectormodels 203. For example, GOS 105 may evaluate metrics based on real-wordand/or simulated KPIs and/or attributes of one or more sectors 101 inorder to generate one or more clusters, classifications, or the likewhich may be reflected by sector models 203.

Process 900 may further include identifying (at 904) one or moreconfiguration frameworks 201 associated with sector models 203. Forexample, as discussed above, GOS 105 may identify configurationframeworks 201, which may include weights for particular KPIs,attributes, or the like. The weights may be used when evaluating overalleffectiveness, yield, etc. of given sectors 101. For example, asdiscussed above, the weights may be applied to KPIs associated withsectors 101 in order to generate an overall optimization scoreassociated with sectors 101. In some embodiments, as noted above, GOS105 may determine one or more affinity scores between particular sectormodels 203 and configuration frameworks 201, which may indicate theeffectiveness, correlation, etc. of a given configuration framework 201to a given sector model 203.

Process 900 may additionally include receiving (at 906) metrics, KPIs,attributes, or the like associated with a particular sector 101. Forexample, GOS 105 may receive the metrics, KPIs, attributes, or the likefrom one or more devices or systems located in sector 101, one or moredevices or systems that provide service to UEs located in sector 101,one or more devices or systems associated with a core network to whichnetwork infrastructure associated with sector 101 is communicativelycoupled, or some other suitable device or system. As discussed above,the metrics, KPIs, attributes, etc., may include, for example, QoSmetrics 501, energy consumption metrics 503, RAN configurationparameters 505, inter-sector information 507, locale features 509,and/or other suitable information.

Process 900 may also include determining (at 908) a particular sectormodel 203 based on the received metrics, KPIs, attributes, etc. Forexample, as discussed above, GOS 105 may use one or more AI/MLtechniques to determine an association, correlation, or the like betweenthe received metrics, KPIs, attributes, etc. and metrics, KPIs,attributes, etc. associated with sector model 203. For example, GOS 105may select a particular sector model 203 from a set of candidate sectormodels 203, and/or may generate a new sector model 203 based on thereceived metrics, KPIs, attributes, etc.

Process 900 may further include selecting (at 910) a particularconfiguration framework 201 based on the determined sector model 203.For example, as discussed above, GOS 105 may select configurationframework 201 based on respective sector-framework affinity scores 511between sector model 203 and a set of candidate configuration frameworks201, and/or may select or generate configuration framework 201 based onsector model 203 (e.g., as similarly discussed above with respect toFIGS. 7A and/or 7B).

Process 900 may additionally include identifying (at 912) a set ofactions 205 to perform based on the selected particular configurationframework 201. For example, as discussed above, GOS 105 may selectactions/parameters 205 based on respective framework-action affinityscores 513 between configuration framework 201 and a set of candidateactions/parameters 205, and/or may otherwise select or generateactions/parameters 205 based on sector model 203. As noted above,actions/parameters 205 may include QoS actions or parameters, antennaactions or parameters, energy consumption actions or parameters, and/orother suitable actions and/or parameters.

Process 900 may also include implementing (at 914) the identified set ofactions. For example, as discussed above, sector 101 may make one ormore adjustments to parameters, physical devices (e.g., antennas), orthe like based on the identified set of actions/parameters 205.

Process 900 may additionally include determining (at 916) an overalloptimization score for sector 101 based on the selected sector model 203and actions/parameters 205. For example, GOS 105 may determine theoverall optimization score based on KPIs received afteractions/parameters 205 are implemented (at 914), and further based onweights specified by configuration framework 201. In this manner, GOS105 may evaluate the effectiveness, accuracy, etc. of the determinedconfiguration framework 201, sector model 203, and/or actions/parameters205 with respect to sector 101.

As shown in FIG. 9, some or all of process 900 may be performed and/orrepeated iteratively. For example, operations 902, 904, 906, and/or 912may be repeated and/or performed based on the determined (at 916)overall optimization score, which may be updated over time based onreal-time and/or near real-time KPIs of sector 101. In this manner, therespective correlations between sector 101, configuration framework 201,sector model 203, and/or actions/parameters 205 may continue to berefined, and sector 101 may be optimized in an automated manner withoutthe need for manual intervention, thereby enhancing the user experienceof users receiving service in sector 101.

Further, in some embodiments, some or all of process 900 may beconcurrently performed as separate processes on separate devices orsystems of a network. For example, GOS 105 may concurrently perform someor all of process 900 at multiple sectors 101. In some embodiments, GOS105 may perform some or all of process 900 at different levels ofhierarchy, which as discussed above may include determining sectormodels 203 for super sectors, as well as associated configurationframeworks 201 and actions and/or parameters 205. For example, variousorchestration, automation, deployment, etc. systems may implementactions and/or parameters 205 at virtualized or containerized instancesof network functions. One example of such an orchestration, automation,deployment, etc. is the open-source Kubernetes system. One or moreorchestration systems may accordingly adjust parameters associated witha RAN, which may be implemented according to an O-RAN environment (e.g.,using a xApp deployment technique, a rApp deployment technique, or someother suitable deployment technique) in some embodiments, as describedbelow.

Accordingly, the analysis and/or adjustment of parameters associatedwith sectors 101, super sectors, and/or other levels of hierarchy in thenetwork may be performed according to different levels of granularityby, or in concert with, a suitable orchestration system. In someembodiments, for example, GOS 105 may identify a super sector that isexhibiting sub-optimal KPIs, and may further identify a particularsector 101 within the super sector that is exhibiting sup-optimal KPIs(e.g., an overall optimization score below a threshold optimizationscore). In such a situation, GOS 105 may perform actions and/or modifyparameters associated with sector 101, without needing to necessarilyperform actions and/or modify parameters associated with otherconstituent sectors 101 of the super sector. In some embodiments, asalso discussed above GOS 105 may perform some or all of process 900 in aprioritized manner, in order to address low-scoring or lowest-scoringsectors 101 (or other delineations of the wireless network) and improvethe performance of such sectors 101.

FIG. 10 illustrates an example environment 1000, in which one or moreembodiments may be implemented. In some embodiments, environment 1000may correspond to a 5G network, and/or may include elements of a 5Gnetwork. In some embodiments, environment 1000 may correspond to a 5GNon-Standalone (“NSA”) architecture, in which a 5G RAT may be used inconjunction with one or more other RATs (e.g., a LTE RAT), and/or inwhich elements of a 5G core network may be implemented by, may becommunicatively coupled with, and/or may include elements of anothertype of core network (e.g., an evolved packet core (“EPC”)). As shown,environment 1000 may include UE 1001, RAN 1010 (which may include one ormore Next Generation Node Bs (“gNBs”) 1011), RAN 1012 (which may includeone or more one or more evolved Node Bs (“eNBs”) 1013), and variousnetwork functions such as Access and Mobility Management Function(“AMF”) 1015, Mobility Management Entity (“MME”) 1016, Serving Gateway(“SGW”) 1017, Session Management Function (“SMF”)/Packet Data Network(“PDN”) Gateway (“PGW”)-Control plane function (“PGW-C”) 1020, PolicyControl Function (“PCF”)/Policy Charging and Rules Function (“PCRF”)1025, Application Function (“AF”) 1030, User Plane Function(“UPF”)/PGW-User plane function (“PGW-U”) 1035, Home Subscriber Server(“HSS”)/Unified Data Management (“UDM”) 1040, and Authentication ServerFunction (“AUSF”) 1045. Environment 1000 may also include one or morenetworks, such as Data Network (“DN”) 1050. Environment 1000 may includeone or more additional devices or systems communicatively coupled to oneor more networks (e.g., DN 1050), such as GOS 105.

The example shown in FIG. 10 illustrates one instance of each networkcomponent or function (e.g., one instance of SMF/PGW-C 1020, PCF/PCRF1025, UPF/PGW-U 1035, HSS/UDM 1040, and/or 1045). In practice,environment 1000 may include multiple instances of such components orfunctions. For example, in some embodiments, environment 1000 mayinclude multiple “slices” of a core network, where each slice includes adiscrete set of network functions (e.g., one slice may include a firstinstance of SMF/PGW-C 1020, PCF/PCRF 1025, UPF/PGW-U 1035, HSS/UDM 1040,and/or 1045, while another slice may include a second instance ofSMF/PGW-C 1020, PCF/PCRF 1025, UPF/PGW-U 1035, HSS/UDM 1040, and/or1045). The different slices may provide differentiated levels ofservice, such as service in accordance with different Quality of Service(“QoS”) parameters.

The quantity of devices and/or networks, illustrated in FIG. 10, isprovided for explanatory purposes only. In practice, environment 1000may include additional devices and/or networks, fewer devices and/ornetworks, different devices and/or networks, or differently arrangeddevices and/or networks than illustrated in FIG. 10. For example, whilenot shown, environment 1000 may include devices that facilitate orenable communication between various components shown in environment1000, such as routers, modems, gateways, switches, hubs, etc.Alternatively, or additionally, one or more of the devices ofenvironment 1000 may perform one or more network functions described asbeing performed by another one or more of the devices of environment1000. Devices of environment 1000 may interconnect with each otherand/or other devices via wired connections, wireless connections, or acombination of wired and wireless connections. In some implementations,one or more devices of environment 1000 may be physically integrated in,and/or may be physically attached to, one or more other devices ofenvironment 1000.

UE 1001 may include a computation and communication device, such as awireless mobile communication device that is capable of communicatingwith RAN 1010, RAN 1012, and/or DN 1050. UE 1001 may be, or may include,a radiotelephone, a personal communications system (“PCS”) terminal(e.g., a device that combines a cellular radiotelephone with dataprocessing and data communications capabilities), a personal digitalassistant (“PDA”) (e.g., a device that may include a radiotelephone, apager, Internet/intranet access, etc.), a smart phone, a laptopcomputer, a tablet computer, a camera, a personal gaming system, an IoTdevice (e.g., a sensor, a smart home appliance, or the like), a wearabledevice, an Internet of Things (“IoT”) device, a Mobile-to-Mobile (“M2M”)device, or another type of mobile computation and communication device.UE 1001 may send traffic to and/or receive traffic (e.g., user planetraffic) from DN 1050 via RAN 1010, RAN 1012, and/or UPF/PGW-U 1035.

RAN 1010 may be, or may include, a 5G RAN that includes one or more basestations (e.g., one or more gNBs 1011), via which UE 1001 maycommunicate with one or more other elements of environment 1000. UE 1001may communicate with RAN 1010 via an air interface (e.g., as provided bygNB 1011). For instance, RAN 1010 may receive traffic (e.g., voice calltraffic, data traffic, messaging traffic, signaling traffic, etc.) fromUE 1001 via the air interface, and may communicate the traffic toUPF/PGW-U 1035, and/or one or more other devices or networks. Similarly,RAN 1010 may receive traffic intended for UE 1001 (e.g., from UPF/PGW-U1035, AMF 1015, and/or one or more other devices or networks) and maycommunicate the traffic to UE 1001 via the air interface. In someembodiments, sector 101 may be implemented by and/or otherwiseassociated with one or more gNBs 1011. For example, in some embodiments,base station 103 may be, or may include, gNB 1011.

RAN 1012 may be, or may include, a LTE RAN that includes one or morebase stations (e.g., one or more eNBs 1013), via which UE 1001 maycommunicate with one or more other elements of environment 1000. UE 1001may communicate with RAN 1012 via an air interface (e.g., as provided byeNB 1013). For instance, RAN 1010 may receive traffic (e.g., voice calltraffic, data traffic, messaging traffic, signaling traffic, etc.) fromUE 1001 via the air interface, and may communicate the traffic toUPF/PGW-U 1035, and/or one or more other devices or networks. Similarly,RAN 1010 may receive traffic intended for UE 1001 (e.g., from UPF/PGW-U1035, SGW 1017, and/or one or more other devices or networks) and maycommunicate the traffic to UE 1001 via the air interface. In someembodiments, sector 101 may be implemented by and/or otherwiseassociated with one or more eNBs 1013. For example, in some embodiments,base station 103 may be, or may include, eNB 1013.

AMF 1015 may include one or more devices, systems, Virtualized NetworkFunctions (“VNFs”), etc., that perform operations to register UE 1001with the 5G network, to establish bearer channels associated with asession with UE 1001, to hand off UE 1001 from the 5G network to anothernetwork, to hand off UE 1001 from the other network to the 5G network,manage mobility of UE 1001 between RANs 1010 and/or gNBs 1011, and/or toperform other operations. In some embodiments, the 5G network mayinclude multiple AMFs 1015, which communicate with each other via theN14 interface (denoted in FIG. 10 by the line marked “N14” originatingand terminating at AMF 1015).

MME 1016 may include one or more devices, systems, VNFs, etc., thatperform operations to register UE 1001 with the EPC, to establish bearerchannels associated with a session with UE 1001, to hand off UE 1001from the EPC to another network, to hand off UE 1001 from anothernetwork to the EPC, manage mobility of UE 1001 between RANs 1012 and/oreNBs 1013, and/or to perform other operations.

SGW 1017 may include one or more devices, systems, VNFs, etc., thataggregate traffic received from one or more eNBs 1013 and send theaggregated traffic to an external network or device via UPF/PGW-U 1035.Additionally, SGW 1017 may aggregate traffic received from one or moreUPF/PGW-Us 1035 and may send the aggregated traffic to one or more eNBs1013. SGW 1017 may operate as an anchor for the user plane duringinter-eNB handovers and as an anchor for mobility between differenttelecommunication networks or RANs (e.g., RANs 1010 and 1012).

SMF/PGW-C 1020 may include one or more devices, systems, VNFs, etc.,that gather, process, store, and/or provide information in a mannerdescribed herein. SMF/PGW-C 1020 may, for example, facilitate in theestablishment of communication sessions on behalf of UE 1001. In someembodiments, the establishment of communications sessions may beperformed in accordance with one or more policies provided by PCF/PCRF1025.

PCF/PCRF 1025 may include one or more devices, systems, VNFs, etc., thataggregate information to and from the 5G network and/or other sources.PCF/PCRF 1025 may receive information regarding policies and/orsubscriptions from one or more sources, such as subscriber databasesand/or from one or more users (such as, for example, an administratorassociated with PCF/PCRF 1025).

AF 1030 may include one or more devices, systems, VNFs, etc., thatreceive, store, and/or provide information that may be used indetermining parameters (e.g., quality of service parameters, chargingparameters, or the like) for certain applications.

UPF/PGW-U 1035 may include one or more devices, systems, VNFs, etc.,that receive, store, and/or provide data (e.g., user plane data). Forexample, UPF/PGW-U 1035 may receive user plane data (e.g., voice calltraffic, data traffic, etc.), destined for UE 1001, from DN 1050, andmay forward the user plane data toward UE 1001 (e.g., via RAN 1010,SMF/PGW-C 1020, and/or one or more other devices). In some embodiments,multiple UPFs 1035 may be deployed (e.g., in different geographicallocations), and the delivery of content to UE 1001 may be coordinatedvia the N9 interface (e.g., as denoted in FIG. 10 by the line marked“N9” originating and terminating at UPF/PGW-U 1035). Similarly,UPF/PGW-U 1035 may receive traffic from UE 1001 (e.g., via RAN 1010,SMF/PGW-C 1020, and/or one or more other devices), and may forward thetraffic toward DN 1050. In some embodiments, UPF/PGW-U 1035 maycommunicate (e.g., via the N4 interface) with SMF/PGW-C 1020, regardinguser plane data processed by UPF/PGW-U 1035.

HSS/UDM 1040 and AUSF 1045 may include one or more devices, systems,VNFs, etc., that manage, update, and/or store, in one or more memorydevices associated with AUSF 1045 and/or HSS/UDM 1040, profileinformation associated with a subscriber. AUSF 1045 and/or HSS/UDM 1040may perform authentication, authorization, and/or accounting operationsassociated with the subscriber and/or a communication session with UE1001.

DN 1050 may include one or more wired and/or wireless networks. Forexample, DN 1050 may include an Internet Protocol (“IP”)-based PDN, awide area network (“WAN”) such as the Internet, a private enterprisenetwork, and/or one or more other networks. UE 1001 may communicate,through DN 1050, with data servers, other UEs 1001, and/or to otherservers or applications that are coupled to DN 1050. DN 1050 may beconnected to one or more other networks, such as a public switchedtelephone network (“PSTN”), a public land mobile network (“PLMN”),and/or another network. DN 1050 may be connected to one or more devices,such as content providers, applications, web servers, and/or otherdevices, with which UE 1001 may communicate.

GOS 105 may include one or more devices, systems, VNFs, or the like thatperform one or more operations described above. For example, GOS 105 maygenerate, receive, refine, etc. one or more models and/or correlationsthereof, apply the models to real-world scenarios (e.g., to sectors 101based on KPIs, parameters, etc. associated with sectors 101), anddetermine one or more actions to perform based on the determined modelsfor such real-world scenarios. GOS 105 may, as discussed above, receivesuitable information from one or more devices or systems associated withsectors 101 (e.g., UE 1001, gNB 1011, eNB 1013, AMF 1015, MME 1016,HSS/UDM 1040, a SCEF, a NEF, and/or one or more other suitable devicesor systems).

FIG. 11 illustrates an example Distributed Unit (“DU”) network 1100,which may be included in and/or implemented by one or more RANs (e.g.,RAN 1010, RAN 1012, or some other RAN). In some embodiments, aparticular RAN may include one DU network 1100. In some embodiments, aparticular RAN may include multiple DU networks 1100. In someembodiments, DU network 1100 may correspond to a particular gNB 1011 ofa 5G RAN (e.g., RAN 1010). In some embodiments, DU network 1100 maycorrespond to multiple gNBs 1011. In some embodiments, DU network 1100may correspond to one or more other types of base stations of one ormore other types of RANs. As shown, DU network 1100 may include CentralUnit (“CU”) 1105, one or more Distributed Units (“DUs”) 1103-1 through1103-N (referred to individually as “DU 1103,” or collectively as “DUs1103”), and one or more Radio Units (“RUs”) 1101-1 through 1101-M(referred to individually as “RU 1101,” or collectively as “RUs 1101”).

CU 1105 may communicate with a core of a wireless network (e.g., maycommunicate with one or more of the devices or systems described abovewith respect to FIG. 10, such as AMF 1015 and/or UPF/PGW-U 1035). In theuplink direction (e.g., for traffic from UEs 1001 to a core network), CU1105 may aggregate traffic from DUs 1103, and forward the aggregatedtraffic to the core network. In some embodiments, CU 1105 may receivetraffic according to a given protocol (e.g., Radio Link Control (“RLC”))from DUs 1103, and may perform higher-layer processing (e.g., mayaggregate/process RLC packets and generate Packet Data ConvergenceProtocol (“PDCP”) packets based on the RLC packets) on the trafficreceived from DUs 1103.

In accordance with some embodiments, CU 1105 may receive downlinktraffic (e.g., traffic from the core network) for a particular UE 1001,and may determine which DU(s) 1103 should receive the downlink traffic.DU 1103 may include one or more devices that transmit traffic between acore network (e.g., via CU 1105) and UE 1001 (e.g., via a respective RU1101). DU 1103 may, for example, receive traffic from RU 1101 at a firstlayer (e.g., physical (“PHY”) layer traffic, or lower PHY layertraffic), and may process/aggregate the traffic to a second layer (e.g.,upper PHY and/or RLC). DU 1103 may receive traffic from CU 1105 at thesecond layer, may process the traffic to the first layer, and providethe processed traffic to a respective RU 1101 for transmission to UE1001.

RU 1101 may include hardware circuitry (e.g., one or more RFtransceivers, antennas, radios, and/or other suitable hardware) tocommunicate wirelessly (e.g., via an RF interface) with one or more UEs1001, one or more other DUs 1103 (e.g., via RUs 1101 associated with DUs1103), and/or any other suitable type of device. In the uplinkdirection, RU 1101 may receive traffic from UE 1001 and/or another DU1103 via the RF interface and may provide the traffic to DU 1103. In thedownlink direction, RU 1101 may receive traffic from DU 1103, and mayprovide the traffic to UE 1001 and/or another DU 1103.

RUs 1101 may, in some embodiments, be communicatively coupled to one ormore Multi-Access/Mobile Edge Computing (“MEC”) devices, referred tosometimes herein simply as (“MECs”) 1107. For example, RU 1101-1 may becommunicatively coupled to MEC 1107-1, RU 1101-M may be communicativelycoupled to MEC 1107-M, DU 1103-1 may be communicatively coupled to MEC1107-2, DU 1103-N may be communicatively coupled to MEC 1107-N, CU 1105may be communicatively coupled to MEC 1107-3, and so on. MECs 1107 mayinclude hardware resources (e.g., configurable or provisionable hardwareresources) that may be configured to provide services and/or otherwiseprocess traffic to and/or from UE 1001, via a respective RU 1101.

For example, RU 1101-1 may route some traffic, from UE 1001, to MEC1107-1 instead of to a core network (e.g., via DU 1103 and CU 1105). MEC1107-1 may process the traffic, perform one or more computations basedon the received traffic, and may provide traffic to UE 1001 via RU1101-1. In this manner, ultra-low latency services may be provided to UE1001, as traffic does not need to traverse DU 1103, CU 1105, and anintervening backhaul network between DU network 1100 and the corenetwork. In some embodiments, MEC 1107 may include, and/or mayimplement, some or all of the functionality described above with respectto GOS 105.

FIG. 12 illustrates an example 0-RAN environment 1200, which maycorrespond to RAN 1010, RAN 1012, and/or DU network 1100. For example,RAN 1010, RAN 1012, and/or DU network 1100 may include one or moreinstances of O-RAN environment 1200, and/or one or more instances ofO-RAN environment 1200 may implement RAN 1010, RAN 1012, DU network1100, and/or some portion thereof. As shown, O-RAN environment 1200 mayinclude Non-Real Time Radio Intelligent Controller (“RIC”) 1201,Near-Real Time RIC 1203, O-eNB 1205, O-CU-Control Plane (“O-CU-CP”)1207, O-CU-User Plane (“O-CU-UP”) 1209, O-DU 1211, O-RU 1213, andO-Cloud 1215. In some embodiments, O-RAN environment 1200 may includeadditional, fewer, different, and/or differently arranged components.

In some embodiments, some or all of the elements of O-RAN environment1200 may be implemented by one or more configurable or provisionableresources, such as virtual machines, cloud computing systems, physicalservers, and/or other types of configurable or provisionable resources.In some embodiments, some or all of O-RAN environment 1200 may beimplemented by, and/or communicatively coupled to, one or more MECs1107.

Non-Real Time RIC 1201 and Near-Real Time RIC 1203 may receiveperformance information (and/or other types of information) from one ormore sources, and may configure other elements of O-RAN environment 1200based on such performance or other information. For example, Near-RealTime MC 1203 may receive performance information, via one or more E2interfaces, from O-eNB 1205, O-CU-CP 1207, and/or O-CU-UP 1209, and maymodify parameters associated with O-eNB 1205, O-CU-CP 1207, and/orO-CU-UP 1209 based on such performance information. Similarly, Non-RealTime MC 1201 may receive performance information associated with O-eNB1205, O-CU-CP 1207, O-CU-UP 1209, and/or one or more other elements ofO-RAN environment 1200 and may utilize machine learning and/or otherhigher level computing or processing to determine modifications to theconfiguration of O-eNB 1205, O-CU-CP 1207, O-CU-UP 1209, and/or otherelements of O-RAN environment 1200. In some embodiments, Non-Real TimeRIC 1201 may generate machine learning models based on performanceinformation associated with O-RAN environment 1200 or other sources, andmay provide such models to Near-Real Time RIC 1203 for implementation.

O-eNB 1205 may perform functions similar to those described above withrespect to eNB 1013. For example, O-eNB 1205 may facilitate wirelesscommunications between UE 1001 and a core network. O-CU-CP 1207 mayperform control plane signaling to coordinate the aggregation and/ordistribution of traffic via one or more DUs 1103, which may includeand/or be implemented by one or more O-DUs 1211, and O-CU-UP 1209 mayperform the aggregation and/or distribution of traffic via such DUs 1103(e.g., O-DUs 1211). O-DU 1211 may be communicatively coupled to one ormore RUs 1101, which may include and/or may be implemented by one ormore O-RUs 1213. In some embodiments, O-Cloud 1215 may include or beimplemented by one or more MECs 1107, which may provide services, andmay be communicatively coupled, to O-CU-CP 1207, O-CU-UP 1209, 0-DU1211, and/or O-RU 1213 (e.g., via an O1 and/or O2 interface).

FIG. 13 illustrates example components of device 1300. One or more ofthe devices described above may include one or more devices 1300. Device1300 may include bus 1310, processor 1320, memory 1330, input component1340, output component 1350, and communication interface 1360. Inanother implementation, device 1300 may include additional, fewer,different, or differently arranged components.

Bus 1310 may include one or more communication paths that permitcommunication among the components of device 1300. Processor 1320 mayinclude a processor, microprocessor, or processing logic that mayinterpret and execute instructions. Memory 1330 may include any type ofdynamic storage device that may store information and instructions forexecution by processor 1320, and/or any type of non-volatile storagedevice that may store information for use by processor 1320.

Input component 1340 may include a mechanism that permits an operator toinput information to device 1300 and/or other receives or detects inputfrom a source external to 1340, such as a touchpad, a touchscreen, akeyboard, a keypad, a button, a switch, a microphone or other audioinput component, etc. In some embodiments, input component 1340 mayinclude, or may be communicatively coupled to, one or more sensors, suchas a motion sensor (e.g., which may be or may include a gyroscope,accelerometer, or the like), a location sensor (e.g., a GlobalPositioning System (“GPS”)-based location sensor or some other suitabletype of location sensor or location determination component), athermometer, a barometer, and/or some other type of sensor. Outputcomponent 1350 may include a mechanism that outputs information to theoperator, such as a display, a speaker, one or more light emittingdiodes (“LEDs”), etc.

Communication interface 1360 may include any transceiver-like mechanismthat enables device 1300 to communicate with other devices and/orsystems. For example, communication interface 1360 may include anEthernet interface, an optical interface, a coaxial interface, or thelike. Communication interface 1360 may include a wireless communicationdevice, such as an infrared (“IR”) receiver, a Bluetooth® radio, or thelike. The wireless communication device may be coupled to an externaldevice, such as a remote control, a wireless keyboard, a mobiletelephone, etc. In some embodiments, device 1300 may include more thanone communication interface 1360. For instance, device 1300 may includean optical interface and an Ethernet interface.

Device 1300 may perform certain operations relating to one or moreprocesses described above. Device 1300 may perform these operations inresponse to processor 1320 executing software instructions stored in acomputer-readable medium, such as memory 1330. A computer-readablemedium may be defined as a non-transitory memory device. A memory devicemay include space within a single physical memory device or spreadacross multiple physical memory devices. The software instructions maybe read into memory 1330 from another computer-readable medium or fromanother device. The software instructions stored in memory 1330 maycause processor 1320 to perform processes described herein.Alternatively, hardwired circuitry may be used in place of or incombination with software instructions to implement processes describedherein. Thus, implementations described herein are not limited to anyspecific combination of hardware circuitry and software.

The foregoing description of implementations provides illustration anddescription, but is not intended to be exhaustive or to limit thepossible implementations to the precise form disclosed. Modificationsand variations are possible in light of the above disclosure or may beacquired from practice of the implementations.

For example, while series of blocks and/or signals have been describedabove (e.g., with regard to FIGS. 1-9), the order of the blocks and/orsignals may be modified in other implementations. Further, non-dependentblocks and/or signals may be performed in parallel. Additionally, whilethe figures have been described in the context of particular devicesperforming particular acts, in practice, one or more other devices mayperform some or all of these acts in lieu of, or in addition to, theabove-mentioned devices.

The actual software code or specialized control hardware used toimplement an embodiment is not limiting of the embodiment. Thus, theoperation and behavior of the embodiment has been described withoutreference to the specific software code, it being understood thatsoftware and control hardware may be designed based on the descriptionherein.

In the preceding specification, various example embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of the possible implementations. Infact, many of these features may be combined in ways not specificallyrecited in the claims and/or disclosed in the specification. Althougheach dependent claim listed below may directly depend on only one otherclaim, the disclosure of the possible implementations includes eachdependent claim in combination with every other claim in the claim set.

Further, while certain connections or devices are shown, in practice,additional, fewer, or different, connections or devices may be used.Furthermore, while various devices and networks are shown separately, inpractice, the functionality of multiple devices may be performed by asingle device, or the functionality of one device may be performed bymultiple devices. Further, multiple ones of the illustrated networks maybe included in a single network, or a particular network may includemultiple networks. Further, while some devices are shown ascommunicating with a network, some such devices may be incorporated, inwhole or in part, as a part of the network.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, groups or other entities, itshould be understood that such information shall be used in accordancewith all applicable laws concerning protection of personal information.Additionally, the collection, storage, and use of such information canbe subject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as can be appropriatefor the situation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various access control,encryption and anonymization techniques for particularly sensitiveinformation.

No element, act, or instruction used in the present application shouldbe construed as critical or essential unless explicitly described assuch. An instance of the use of the term “and,” as used herein, does notnecessarily preclude the interpretation that the phrase “and/or” wasintended in that instance. Similarly, an instance of the use of the term“or,” as used herein, does not necessarily preclude the interpretationthat the phrase “and/or” was intended in that instance. Also, as usedherein, the article “a” is intended to include one or more items, andmay be used interchangeably with the phrase “one or more.” Where onlyone item is intended, the terms “one,” “single,” “only,” or similarlanguage is used. Further, the phrase “based on” is intended to mean“based, at least in part, on” unless explicitly stated otherwise.

What is claimed is:
 1. A device, comprising: one or more processorsconfigured to: maintain a plurality of sector models, wherein eachsector model is associated with a respective set of radio access network(“RAN”) attributes; maintain a plurality of optimization goals, whereineach sector model is associated with one or more optimization goal ofthe plurality of optimization goals; select, based on a particular setof RAN attributes of a particular RAN, a particular sector model of theplurality of sector models; select a particular optimization goal, ofthe plurality of optimization goals, with which the selected particularsector model is associated; identify a set of actions to perform basedon the particular optimization goal; and implement the set of actions,associated with the particular optimization goal, at the particular RAN.2. The device of claim 1, wherein the particular set of RAN attributesincludes at least one of: performance metrics, or energy consumptionmetrics, wherein selecting the particular sector model includesdetermining that measure of similarity, between performance metrics orenergy consumption metrics of the particular sector model and of theparticular RAN, exceeds a threshold measure of similarity.
 3. The deviceof claim 1, wherein the particular set of RAN attributes includes a setof locale features associated with the particular RAN, wherein selectingthe particular sector model includes determining that measure ofsimilarity, between locale features of the particular sector model andof the particular RAN, exceeds a threshold measure of similarity.
 4. Thedevice of claim 3, wherein the locale features associated with theparticular RAN include at least one of: building density in ageographical area with which the particular RAN is associated,topographical features of the geographical area with which theparticular RAN is associated, or air quality metrics of the geographicalarea with which the particular RAN is associated.
 5. The device of claim1, wherein the one or more processors are further configured to:determine a plurality of sets of affinity scores between the pluralityof optimization goals and a plurality of sets of actions, wherein aparticular set of affinity scores is associated with: the particularoptimization goal, and the plurality of sets of actions, wherein eachrespective affinity score of the particular set of affinity scores isassociated with the particular optimization goal and a respective actionof the plurality of sets of actions.
 6. The device of claim 5, whereinidentifying the set of actions based on the particular configurationframework includes: determining that the respective affinity scoreassociated with the identified set of actions and the particularoptimization goal is a highest affinity score of the particular set ofaffinity scores.
 7. The device of claim 1, wherein each optimizationgoal, of the plurality of optimization goals includes, a plurality ofmetrics that are each associated with a particular weight.
 8. Anon-transitory computer-readable medium, storing a plurality ofprocessor-executable instructions to: maintain a plurality of sectormodels, wherein each sector model is associated with a respective set ofradio access network (“RAN”) attributes; maintain a plurality ofoptimization goals, wherein each sector model is associated with one ormore optimization goal of the plurality of optimization goals; select,based on a particular set of RAN attributes of a particular RAN, aparticular sector model of the plurality of sector models; select aparticular optimization goal, of the plurality of optimization goals,with which the selected particular sector model is associated; identifya set of actions to perform based on the particular optimization goal;and implement the set of actions, associated with the particularoptimization goal, at the particular RAN.
 9. The non-transitorycomputer-readable medium of claim 8, wherein the particular set of RANattributes includes at least one of: performance metrics, or energyconsumption metrics, wherein selecting the particular sector modelincludes determining that measure of similarity, between performancemetrics or energy consumption metrics of the particular sector model andof the particular RAN, exceeds a threshold measure of similarity. 10.The non-transitory computer-readable medium of claim 8, wherein theparticular set of RAN attributes includes a set of locale featuresassociated with the particular RAN, wherein selecting the particularsector model includes determining that measure of similarity, betweenlocale features of the particular sector model and of the particularRAN, exceeds a threshold measure of similarity.
 11. The non-transitorycomputer-readable medium of claim 10, wherein the locale featuresassociated with the particular RAN include at least one of: buildingdensity in a geographical area with which the particular RAN isassociated, topographical features of the geographical area with whichthe particular RAN is associated, or air quality metrics of thegeographical area with which the particular RAN is associated.
 12. Thenon-transitory computer-readable medium of claim 8, wherein theplurality of processor-executable instructions further includeprocessor-executable instructions to: determine a plurality of sets ofaffinity scores between the plurality of optimization goals and aplurality of sets of actions, wherein a particular set of affinityscores is associated with: the particular optimization goal, and theplurality of sets of actions, wherein each respective affinity score ofthe particular set of affinity scores is associated with the particularoptimization goal and a respective action of the plurality of sets ofactions.
 13. The non-transitory computer-readable medium of claim 12,wherein identifying the set of actions based on the particularconfiguration framework includes: determining that the respectiveaffinity score associated with the identified set of actions and theparticular optimization goal is a highest affinity score of theparticular set of affinity scores.
 14. The non-transitorycomputer-readable medium of claim 8, wherein each optimization goal, ofthe plurality of optimization goals includes, a plurality of metricsthat are each associated with a particular weight.
 15. A method,comprising: maintaining a plurality of sector models, wherein eachsector model is associated with a respective set of radio access network(“RAN”) attributes; maintaining a plurality of optimization goals,wherein each sector model is associated with one or more optimizationgoal of the plurality of optimization goals; selecting, based on aparticular set of RAN attributes of a particular RAN, a particularsector model of the plurality of sector models; selecting a particularoptimization goal, of the plurality of optimization goals, with whichthe selected particular sector model is associated; identifying a set ofactions to perform based on the particular optimization goal; andimplementing the set of actions, associated with the particularoptimization goal, at the particular RAN.
 16. The method of claim 15,wherein the particular set of RAN attributes includes at least one of:performance metrics, or energy consumption metrics, wherein selectingthe particular sector model includes determining that measure ofsimilarity, between performance metrics or energy consumption metrics ofthe particular sector model and of the particular RAN, exceeds athreshold measure of similarity.
 17. The method of claim 15, wherein theparticular set of RAN attributes includes a set of locale featuresassociated with the particular RAN, wherein selecting the particularsector model includes determining that measure of similarity, betweenlocale features of the particular sector model and of the particularRAN, exceeds a threshold measure of similarity.
 18. The method of claim15, the method further comprising: determining a plurality of sets ofaffinity scores between the plurality of optimization goals and aplurality of sets of actions, wherein a particular set of affinityscores is associated with: the particular optimization goal, and theplurality of sets of actions, wherein each respective affinity score ofthe particular set of affinity scores is associated with the particularoptimization goal and a respective action of the plurality of sets ofactions.
 19. The method of claim 18 wherein identifying the set ofactions based on the particular configuration framework includes:determining that the respective affinity score associated with theidentified set of actions and the particular optimization goal is ahighest affinity score of the particular set of affinity scores.
 20. Themethod of claim 15, wherein each optimization goal, of the plurality ofoptimization goals includes, a plurality of metrics that are eachassociated with a particular weight.